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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi Jurnal Teknologi Elektro InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Abdi Masyarakat (JAM) Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE) Internet of Things and Artificial Intelligence Journal
Rachmat Muwardi
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Sinergi

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Defect classification of radius shaping in the tire curing process using Fine-Tuned Deep Neural Network Zendi Iklima; Bugi Nur Rohman; Rahmat Muwardi; Asif Khan; Zody Arifiansyah
SINERGI Vol 26, No 3 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2022.3.009

Abstract

The curing process or vulcanization process is the final stage of the tire manufacturing process, where the properties of the tire compound change from rubber-plastic material to become elastic by forming cross-links in its molecular structure. The green tire is formed in the curing process, which is placed on the bottom mould. The inside of the green tire surrounds the bladder. The top mould will close to carry out the next curing process. In closing the mould, there is a shaping process of forming a green tire placed on the bladder and given a proportional pressure. Improper or abnormal radius shaping results cause seventy percent of product defects. This paper proposed abnormal detection of radius shaping in the curing process using Fine-tuned Deep Neural Network (DNN). Several DNN models have been examined to analyze an optimized DNN model for abnormal detection of radius shaping in the curing process. The fine-tuned DNN architecture has been exported for the curing system. The DNN was trained with a training accuracy of 97.88%, a validation accuracy of 95%, a testing accuracy of 100%, and a loss of 4.93%.
Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family Mirna Yunita; Rachmat Muwardi; Zendi Iklima
SINERGI Vol 27, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023..1.004

Abstract

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.   
Co-Authors Akhmad Wahyu Dani Alvin Sepbrian Andika Arrizki Asif Khan Bugi Nur Rohman Danang Widya Pratama Diah Septiyana Firman Andika Freddy Artadima Silaban Galang P. N. Hakim Galang Persada Nurani Hakim Galatia Erica Yehezkiel Gao Hongmin Hafid Ihsan Harun Usman Ghifarsyam Harun Usman Ghifarsyam Hendy Juliyanto Heri Santoso, Makmur Hongmin Gao Hongmin Gao Joe Mada Ranseda Permana Julpri Andika Ketty Siti Salamah Lenni Lenni Lenni Lenni Lukman Medriavin Silalahi Mhd Adanan Purba Mirna Yunita Mirna Yunita Muhammad Hafizd Ibnu Hajar Mustain Rhozaly Ninda Sari Reyhan Rahmat Adisaputro Septiyana, Diah Setiyo Budiyanto Suwoyo, Heru Triyanto Pangaribowo Yosy Rahmawati Yudhi Gunardi Yuliza Yuliza Yuliza Yuliza Yuliza Yuliza Zendi Iklima Zendi Iklima Zody Arifiansyah